Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
摘要 作者提出了一种只使用整数运算的quantization方式，比起float point运算效率更高。同时提出了一种相应的训练方式来保证quantization之后的准确率。这篇文章的方法提升了accuracy和on-device latency之间的trade off，并且可以在MobileNets上使用。 1 introduction 作者总结了目前有效将庞大的神经网络应用在资源更为有限的
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相关文章
- 1. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 2. 【论文阅读笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. Bag of Freebies for Training Object Detection Neural Networks
- 5. （转）A Recipe for Training Neural Networks
- 6. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 7. Domain-Adversarial Training of Neural Networks
- 8. DANN：Domain-Adversarial Training of Neural Networks
- 9. Weighted-Entropy-based Quantization for Deep Neural Networks
- 10. Strategies For Pre-Training Graph Neural Networks